Well that was disappointing. I’ve read some of George Saunders’s short stories
and was entertained, but I didn’t much enjoy Lincoln in the Bardo. It’s the story of Abraham Lincoln
coming to the graveyard to visit his newly dead son William, told from the
perspective of a variety of lost souls that don’t believe they’re dead. There
was no plot to speak of, and none of the large cast of characters was appealing.
I did enjoy the sections that were fictional quotes from contemporary histories,
many of which contradicted each other on the details, and some of the characters
told funny stories, but it didn’t hold together as a novel.

Widely acclaimed, winner of the Man Booker Prize, on many best of 2017 lists.
Not my cup of tea.

It’s one day until The Tournament of Books announces the list of books for
this year’s competition, and I’ve been reading some of the Long List, including the
book commented on here, Elan Mastai’s All Our Wrong Todays. I throughly enjoyed it. The writing
sparkles, the narrator is hilarously self-deprecating, and because of the
premise, there is a lot of insightful commentary about contemporary society.

The main plot line is that the main character grew up in an alternative timeline
where a device that produces free energy was invented in 1965 and put into the
public domain. With free energy and fifty plus years, his world is something of
a techonological utopia (especially compared with our present). However, for
reasons best left unspoiled, he alters the timeline and is stuck here in our
timeline with the rest of us.

The narrator on waking up for the first time in our timeline:

Here, it’s like nobody has considered using even the most rudimentary
technology to improve the process. Mattresses don’t subtly vibrate
to keep your muscles loose. Targeted steam valves don’t clean your
body in slumber. I mean, blankets are made from tufts of plant fiber
spun into thread and occasionally stuffed with feathers. Feathers. Like
from actual birds.

While there’s a lot of science-fiction concepts in the story, it’s really more
of a love story than what it sounds like it’d be. There were a couple plot
points I probably would have written differently, but the book is really funny,
touching and thoughful. I highly recommend it. Best book I’ve read in 2018 so
far…

A couple other quotes I found particularly timely:

Part of the problem is this world is basically a cesspool of misogyny, male
entitlement, and deeply demented gender constructs accepted as casual fact by
outrageously large swaths of the human population.

and

People are despondent about the future because they’re increasingly aware
that we, as a species, chased an inspiring dream that led us to ruin. We told
ourselves the world is here for us to control, so the better our technology,
the better our control, the better our world will be. The fact that for every
leap in technology the world gets more sour and chaotic is deeply confusing.
The better things we build keep making it worse. The belief that the world is
here for humans to control is the philosophical bedrock of our civilization,
but it’s a mistaken belief. Optimism is the pyre on which we’ve been setting
ourselves aflame.

Introduction

I’m planning a short trip to visit family in Florida and thought I’d
take advantage of being in a new place to do some late winter
backpacking where it’s warmer than in Fairbanks. I think I’ve settled on
a 3‒5 day backpacking trip in Big South Fork National River and
Recreation Area, which is in northeastern Tennesee and southeastern
Kentucky.

Except for a couple summer trips in New England in the 80s, my backpacking
experience has been in summer, in places where it doesn’t rain much and is
typically hot and dry (California, Oregon). So I’d like to find out what the
weather should be like when I’m there.

Data

I’ll use the Global Historical Climatology Network —
Daily dataset,
which contains daily weather observations for more than 100 thousand
stations across the globe. There are more than 26 thousand active
stations in the United States, and data for some U.S. stations goes back
to 1836. I loaded the entire dataset—2.4 billion records as of last
week—into a PostgreSQL database, partitioning the data by year. I’m
interested in daily minimum and maximum temperature (TMIN,
TMAX), precipitation (PRCP) and snowfall (SNOW), and in
stations within 50 miles of the center of the recreation area.

The following map shows the recreation area boundary (with some strange
drawing errors, probably due to using the fortify command) in green,
the Tennessee/Kentucky border across the middle of the plot, and the
19 stations used in the analysis.

Here are the details on the stations:

station_id

station_name

start_year

end_year

latitude

longitude

miles

USC00407141

PICKETT SP

2000

2017

36.5514

-84.7967

6.13

USC00406829

ONEIDA

1959

2017

36.5028

-84.5308

9.51

USC00400081

ALLARDT

1928

2017

36.3806

-84.8744

12.99

USC00404590

JAMESTOWN

2003

2017

36.4258

-84.9419

14.52

USC00157677

STEARNS 2S

1936

2017

36.6736

-84.4792

16.90

USC00401310

BYRDSTOWN

1998

2017

36.5803

-85.1256

24.16

USC00406493

NEWCOMB

1999

2017

36.5517

-84.1728

29.61

USC00158711

WILLIAMSBURG 1NW

2011

2017

36.7458

-84.1753

33.60

USC00405332

LIVINGSTON RADIO WLIV

1961

2017

36.3775

-85.3364

36.52

USC00154208

JAMESTOWN WWTP

1971

2017

37.0056

-85.0617

39.82

USC00406170

MONTEREY

1904

2017

36.1483

-85.2650

40.04

USC00406619

NORRIS

1936

2017

36.2131

-84.0603

41.13

USC00402202

CROSSVILLE ED & RESEARCH

1912

2017

36.0147

-85.1314

41.61

USW00053868

OAK RIDGE ASOS

1999

2017

36.0236

-84.2375

42.24

USC00401561

CELINA

1948

2017

36.5408

-85.4597

42.31

USC00157510

SOMERSET 2 N

1950

2017

37.1167

-84.6167

42.36

USW00003841

OAK RIDGE ATDD

1948

2017

36.0028

-84.2486

43.02

USW00003847

CROSSVILLE MEM AP

1954

2017

35.9508

-85.0814

43.87

USC00404871

KINGSTON

2000

2017

35.8575

-84.5278

45.86

To perform the analysis, I collected all valid observations for the stations
listed, then reduced the results, including observations where the day of the
year was between 45 and 52 (February 14‒21).

variable

observations

PRCP

5,942

SNOW

5,091

TMAX

4,900

TMIN

4,846

Results

Temperature

We will consider temperature first. The following two plots show the
distribution of daily minimum and maximum temperatures. In both plots,
the bars represent the number of observations at that temperature, the
vertical red line through the middle of the plot shows the average
temperature, and the light orange and blue sections show the ranges of
temperatures enclosing 80% and 98% of the data.

The minimum daily temperature figure shows that the average minimum
temperature is below freezing, (28.9 °F) and eighty percent of all days
in the third week of February were between 15 and 43 °F (the light
orange region). The minimum temperature was colder than 15 °F or warmer
than 54 °F 2% of the time (the light blue region). Maximum daily
temperature was an average of 51 °F, and was rarely below freezing
or above 72 °F.

Another way to look at this sort of data is to count particular occurances and
divide by the total, “binning” the data into groups. Here we look at the number
of days that were below freezing, colder than 20 °F or colder than 10 °F.

temperature

observed days

percent chance

below freezing

3,006

62.0

colder than 20

1,079

22.3

colder than 10

203

4.2

TOTAL

4,846

100.0

What about the daily maximum temperature?

temperature

observed days

percent chance

colder than 20

22

0.4

below freezing

371

7.6

below 40

1,151

23.5

above 50

2,569

52.4

above 60

1,157

23.6

above 70

80

1.6

TOTAL

4,900

100.0

The chances of it being below freezing during the day are pretty slim,
and more than half the time it’s warmer than 50 °F, so even if it’s cold
at night, I should be able to get plenty warm hiking during the day.

Precipitation

How often it rains, and how much falls when it does is also important
for planning a successful backpacking trip. Most of my backpacking has
been done in the summer in California, where rainfall is rare and even
when it does rain, it’s typically over quickly. Daily weather data can’t
tell us about the hourly pattern of rainfall, but we can find out how
often and how much it has rained in the past.

rainfall amount

observed days

percent chance

raining

2,375

40.0

tenth

1,610

27.1

quarter

1,136

19.1

half

668

11.2

inch

308

5.2

TOTAL

5,942

100.0

This data shows that the chance of rain on any given day between
February 14th and the 21st is 40%, and the chance of getting at least a
tenth of an inch is 30%. That’s certainly higher than in the Sierra
Nevada in July, although by August, afternoon thunderstorms are more
common in the mountains.

When there is precipitation, the distribution of precipitation totals
looks like this:

cumulative frequency

precipition

1%

0.01

5%

0.02

10%

0.02

25%

0.07

50%

0.22

75%

0.59

90%

1.18

95%

1.71

99%

2.56

These numbers are cumulative which means that on 1 percent of the days
with precipition, there was a hundredth of an inch of liquid
precipitation or less. Ten percent of the days had 0.02 inches or
less. And 50 percent of rainy days had 0.22 inches or liquid
precipitation or less. Reading the numbers from the top of the
distribution, there was more than an inch of rain 10 percent of the days
on which it rained, which is a little disturbing.

One final question about precipitation is how long it rains once it
starts raining? Do we get little showers here and there, or are there
large storms that dump rain for days without a break? To answer this
question, I counted the number of days between zero-rainfall days, which
is equal to the number of consecutive days where it rained.

consecutive days

percent chance

1

53.0

2

24.4

3

11.9

4

7.5

5

2.2

6

0.9

7

0.1

The results show that more than half the time, a single day of rain is
followed by at least one day without. And the chances of having it rain
every day of a three day trip to this area in mid-February is 11.9%.

Snowfall

Repeating the precipitation analysis with snowfall:

snowfall amount

observed days

percent chance

snowing

322

6.3

inch

148

2.9

two

115

2.3

TOTAL

5,091

100.0

Snowfall isn’t common on these dates, but it did happen, so I will need to be
prepared for it. Also, the PRCP variable includes melted snow, so a small
portion of the precipitation from the previous section overlaps with the
snowfall shown here.

Conclusion

Based on this analysis, a 3‒5 day backpacking trip to the Big South Fork
National River and Recreation area seems well within my abilities and my
gear. It will almost certainly be below freezing at night, but isn’t
likely to be much below 20 °F, snowfall is uncommon, and even though
I will probably experience some rain, it shouldn’t be too much or
carry on for the entire trip.

Appendix

The R code for this analysis appears below. I’ve loaded the GHCND data
into a PostgreSQL database with observation data partitioned by year.
The database tables are structured basically as they come from the
National Centers for Environmental Information.

Yesterday we lost Koidern to complications from laryngeal paralysis. Koidern
came to us in 2006 from Andrea’s mushing partner who thought she was too
“ornery.” It is true that she wouldn’t hesitate to growl at a dog or cat who got
too close to her food bowl, and she was protective of her favorite bed, but in
every other way she was a very sweet dog. When she was younger she loved to
give hugs, jumping up on her hind legs and wrapping her front legs around your
waist. She was part Saluki, which made her very distinctive in Andrea’s dog
teams and she never lost her beautiful brown coat, perky ears, and curled tail.
I will miss her continual energy in the dog yard racing around after the other
dogs, how she’d pounce on dog bones and toss them around, “smash” the cats, and
the way she’d bark right before coming into the house as if to announce her
entrance.

Introduction

The Alaska Department of Transportation is working on updating their bicycling
and pedestrian master plan for the state and
their web site mentions Alaska as having high percentages of bicycle and
pedestrian commuters relative to the rest of the country. I’m interested because
I commute to work by bicycle (and occasionally ski or run) every day, either on
the trails in the winter, or the roads in the summer. The company I work for
(ABR) pays it’s employees $3.50 per day for using
non-motorized means of transportation to get to work. I earned more than $700
last year as part of this program and ABR has paid it’s employees almost $40K
since 2009 not to drive to work.

The Census Bureau keeps track of how people get to work in the American
Community Survey, easily accessible from their web site. We’ll use this data to
see if Alaska really does have higher than average rates of non-motorized
commuters.

Data

The data comes from
FactFinder.
I chose ‘American Community Survey’ from the list of data sources near
the bottom, searched for ‘bicycle’, chose ‘Commuting characteristics by
sex’ (Table S0801), and added the ‘All States within United States and
Puerto Rico’ as the Geography of interest. The site generates a zip file
containing the data as a CSV file along with several other informational
files. The code for extracting the data appears at the bottom of this
post.

The data are percentages of workers 16 years and over and their means of
transportation to work. Here’s a table showing the top 10 states ordered
by the combination of bicycling and walking percentage.

state

total

motorized

carpool

public_trans

walk

bicycle

1

District of Columbia

358,150

38.8

5.2

35.8

14.0

4.1

2

Alaska

363,075

80.5

12.6

1.5

7.9

1.1

3

Montana

484,043

84.9

10.4

0.8

5.6

1.6

4

New York

9,276,438

59.3

6.6

28.6

6.3

0.7

5

Vermont

320,350

85.1

8.2

1.3

5.8

0.8

6

Oregon

1,839,706

81.4

10.2

4.8

3.8

2.5

7

Massachusetts

3,450,540

77.6

7.4

10.6

5.0

0.8

8

Wyoming

289,163

87.3

10.0

2.2

4.6

0.6

9

Hawaii

704,914

80.9

13.5

7.0

4.1

0.9

10

Washington

3,370,945

82.2

9.8

6.2

3.7

1.0

Alaska has the second highest rates of walking and biking to work behind
the District of Columbia. The table is an interesting combination of
states with large urban centers (DC, New York, Oregon, Massachusetts)
and those that are more rural (Alaska, Montana, Vermont, Wyoming).

Another way to rank the data is by combining all forms of transportation
besides single-vehicle motorized transport (car pooling, public
transportation, walking and bicycling).

state

total

motorized

carpool

public_trans

walk

bicycle

1

District of Columbia

358,150

38.8

5.2

35.8

14.0

4.1

2

New York

9,276,438

59.3

6.6

28.6

6.3

0.7

3

Massachusetts

3,450,540

77.6

7.4

10.6

5.0

0.8

4

New Jersey

4,285,182

79.3

7.5

11.6

3.3

0.3

5

Alaska

363,075

80.5

12.6

1.5

7.9

1.1

6

Hawaii

704,914

80.9

13.5

7.0

4.1

0.9

7

Oregon

1,839,706

81.4

10.2

4.8

3.8

2.5

8

Illinois

6,094,828

81.5

7.9

9.3

3.0

0.7

9

Washington

3,370,945

82.2

9.8

6.2

3.7

1.0

10

Maryland

3,001,281

82.6

8.9

9.0

2.6

0.3

Here, the states with large urban centers come out higher because of the
number of commuters using public transportation. Despite very low
availability of public transportation, Alaska still winds up 5th on this
list because of high rates of car pooling, in addition to walking and
bicycling.

Map data

To look at regional patterns, we can make a map of the United States
colored by non-motorized transportation percentage. This can be a little
challenging because Alaska and Hawaii are so far from the rest of the
country. What I’m doing here is loading the state data, transforming the
data to a projection that’s appropriate for Alaska, and moving Alaska
and Hawaii closer to the lower-48 for display. Again, the code appears
at the bottom.

You can see that non-motorized transportation is very low throughout the
deep south, and tends to be higher in the western half of the country,
but the really high rates of bicycling and walking to work are isolated.
High Vermont next to low New Hampshire, or Oregon and Montana split by
Idaho.

Urban and rural, median age of the population

What explains the high rates of non-motorized commuting in Alaska and
the other states at the top of the list? Urbanization is certainly one
important factor explaining why the District of Columbia and states like
New York, Oregon and Massachusetts have high rates of walking and
bicycling. But what about Montana, Vermont, and Wyoming?

Age of the population might have an effect as well, as younger people
are more likely to walk and bike to work than older people. Alaska has
the second youngest population (33.3 years) in the U.S. and DC is third
(33.8), but the other states in the top five (Utah, Texas, North Dakota)
don’t have high non-motorized transportation.

So it’s more complicated that just these factors. California is a good
example, with a combination of high urbanization (second, 95.0% urban),
low median age (eighth, 36.2) and great weather year round, but is 19th
for non-motorized commuting. Who walks in California, after all?

Conclusion

I hope DOT comes up with a progressive plan for improving opportunities
for pedestrian and bicycle transportation in Alaska They’ve made some
progress here in Fairbanks; building new paths for non-motorized
traffic; but they also seem blind to the realities of actually using the
roads and paths on a bicycle. The “bike path” near my house abruptly
turns from asphalt to gravel a third of the way down Miller Hill, and
the shoulders of the roads I commute on are filled with deep snow in
winter, gravel in spring, and all manner of detritus year round. Many
roads don’t have a useable shoulder at all.